Preparing for a Data Science Transformation

In an era where Artificial Intelligence is reshaping the way we do business, and where technology is only as smart as the analytics that powers it, it’s now widely understood that data science gives organizations a clear competitive edge.

If there was ever any real doubt, the argument in favor of embedding data and intelligent technology at the core of business has been conclusively settled. In an era where Artificial Intelligence is reshaping the way we do business, and where technology is only as smart as the analytics that powers it, it’s now widely understood that data science gives organizations a clear competitive edge.

Take a leading e-commerce retailer that provided personalized recommendations to each of their customers and drove nearly $1B in incremental annual revenue. Or a European telco that dramatically increased its conversion rate – by up to 50% -- by using real-time data to target customers with the most compelling content in the most relevant channels.

But organizations can only expect to see these benefits once they are truly ‘data-science-ready’. That means embracing new ways of embedding data science across business functions and processes. If they do, the benefits far outweigh the investment.

In this column, we will explore five different aspects of data science transformation and examine how analytics and intelligent technology create a solid foundation for success – today and in the future. Read on for a sneak peek.

1. Rethink HR. Many companies struggle to acquire, develop, and retain aggressively sought-after data science and analytics talent. And these challenges will only grow more acute as demand continues to grow. The uniqueness of data science talent calls for a whole new approach to HR, which means investing in specialized recruiters and structuring relevant compensation and incentives to land the best candidates. But even companies that manage to get these steps right subsequently fail to invest (enough) in talent development and retention. It’s crucial for organizations to map out clear career paths and invest in a learning culture that will retain the best data scientists.

2. Data Science. Integrate it. Analytics has become too valuable to be a back-room function, decoupled from the daily business. A 2017 Burchworks survey revealed that the reason why nearly 50 percent of data scientists said they’d left their last employer was the absence of a data culture. Organizations need to integrate data science into the wider business, give data scientists “seat at the table” alongside key decision-makers across the company, and have data-driven insights informing decisions throughout the organization.

3. Unlock new intelligence from your data. Siloed data leads to siloed analysis which, in turn, leads to limited insights. All too often, the impact data scientists can make through their analyses is limited by the fact that they can only tap a fraction of an organization’s data. To unlock value with data science, organizations first need to empower their developers to create a unified view of the customer, in a single database, filled with clean, structured and secure data.

4. Industrializing the data science playground. Even when they manage to hire great data scientists, many companies limit that talent by making them work with outdated technology, which lacks the size, speed, scalability and security needed for a long-term enterprise solution. While upgrading infrastructure can come with a hefty price tag, it doesn’t have to. Some technology providers offer proof-of-concept trials or elastic pricing that allows companies to test the technology. What’s more, many simple, low-cost investments can still have a transformative impact on the business.

5. Operating with new agility. Achieving rapid value and ROI from analytics and data science talent requires a new type of delivery model. Fundamentally, that means an agile model. What makes it different? Code ships frequently; prototypes are tested sooner and data from real users provides actionable feedback. All these will help data science teams be far more effective.

We’ll explore each of these five areas in more detail, but keep the following in mind: Data science transformation is a journey, not a destination. And its impact is greater than the sum of its five parts if you approach it holistically, across your enterprise.

Robert Berkeyis a managing director at Accenture Applied Intelligence, where he leads the Strategy & Transformation offering globally.Dr. Amy Gershkoff is a data consultant; she was previously Chief Data Officer for companies including WPP, Data Alliance, Zynga, and Ancestry.com.

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